Health AI Central
Share:
Peer-Reviewed Publication
Mol Breed2026;46(9):89.September 1, 2026Journal Article

SNPoptimizer: a scalable genetic-algorithm framework to derive minimal discriminatory SNP panels from large genotyping datasets.

Salvatore Esposito1, Nicola Scalzi1,2, Samuela Palombieri2,3, Walter Sanseverino4, Francesco Sestili2,3, Alessandra Stella5, Raffaella Balestrini6, Stefania Grillo1, Ray Anthony Bressan7, Giorgia Batelli1
1Institute of Biosciences and Bioresources Research Division Portici, National Research Council of Italy (CNR-IBBR), Via Università, 133, Portici, Italy.
2Department of Agriculture and Forest Sciences (DAFNE), University of Tuscia, Via S. Camillo de Lellis, Viterbo, Italy.
3Valuegroovers srl, Via S. Camillo de Lellis, Viterbo, Italy.
4Sequentia Biotech, Carrer de València, Barcelona, SL Spain.
5Institute of Agricultural Biology and Biotechnology, National Research Council of Italy (CNR-IBBA), Via Alfonso Corti, 12, Milan, Italy.
6Institute of Biosciences and Bioresources, National Research Council of Italy (CNR-IBBR), Via Amendola 165/A, Bari, Italy.
7West Lafayette, IN USA.
PubMed PMC DOI PDF

Abstract

UNLABELLED: The ability to efficiently discriminate genotypes is a critical step in genomics-assisted breeding, population genomics, biodiversity studies, traceability along food chains, and germplasm management. However, identifying the minimal and most informative subset of SNPs capable of uniquely distinguishing a large set of individuals remains a computationally challenging task. Here, we pre…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.